Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Робастная модель EGARCH× | Модель TGARCH (Threshold GARCH)× | |
|---|---|---|
| Область | Эконометрика | Эконометрика |
| Семейство | Regression model | Regression model |
| Год появления≠ | 2008 | 1993-1994 |
| Автор метода≠ | Nelson (1991) for EGARCH; robust adaptation via Muler & Yohai (2008) and related authors | Zakoian (1994); Glosten, Jagannathan & Runkle (1993) |
| Тип≠ | Robust volatility model | Asymmetric volatility model |
| Основополагающий источник≠ | Muler, N., & Yohai, V. J. (2008). Robust estimates for GARCH models. Journal of Statistical Planning and Inference, 138(10), 2918–2940. DOI ↗ | Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955. DOI ↗ |
| Другие названия | Robust EGARCH model, outlier-robust EGARCH, robust exponential GARCH, REGARCH | Threshold GARCH, TGARCH, GJR-GARCH, asymmetric GARCH |
| Связанные | 6 | 6 |
| Сводка≠ | Robust EGARCH extends Nelson's (1991) Exponential GARCH model by replacing standard quasi-maximum likelihood estimation with outlier-resistant procedures — typically bounded-influence or M-estimation — so that a small fraction of extreme observations or data errors cannot distort the estimated volatility dynamics or the leverage effect. | The Threshold GARCH (TGARCH) model extends the standard GARCH framework by allowing positive and negative return shocks to have asymmetric effects on conditional variance. Negative shocks — bad news — typically amplify volatility more than positive shocks of the same magnitude, a stylised fact known as the leverage effect. TGARCH captures this asymmetry through a threshold indicator that switches on when the previous period's shock was negative. |
| ScholarGateНабор данных ↗ |
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